From the DBMS to the Governance Catalog: The Point of Control Is Moving
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| Image by Geralt (Pixabay) |
In the last couple of years, I have seen one of the most consequential shifts in the modern data platform landscape market happening quietly but decisively: the point of control is moving away from the database management system (DBMS) and toward the governance catalog.
This transition is not
accidental. It seems to be driven by three reinforcing forces: the industry’s
move toward open data models, the growing necessity of governance in the AI
era, and the strategic positioning of major players such as Databricks and Snowflake, among others.
Together, these forces
are redefining where power, control, and differentiation sit in the data stack.
What follows is a brief
look at why these matter and why it is happening now.
From Execution Control to Governance
Control
Traditionally, the
DBMS served as the primary control plane of the data platform. It enforced
access policies, managed read-write operations, and effectively functioned as
the system of record for both data and governance.
Today, this assumption
no longer holds. As data estates expand across cloud services, object storage,
and multiple execution engines, control is being taken away from execution and
elevated to governance. With the governance catalog, or data governance
catalog, emerging as the authoritative system of truth for metadata, policies,
lineage, and access rules across the entire data environment.
Of course, execution
engines still matter, but their role is becoming more interchangeable; read-write
operations can be managed by lighter-weight, embedded, or lower-cost engines,
while governance defines who can access what, how, and for
what purpose.
This distinction is
critical; the shift underway is not about where computation happens, but about
where control is defined and enforced, especially in the era of artificial
intelligence (AI).
Open Data Models Make Governance
Central
But how are we coming
to this situation? Well, this change would not be possible without the
widespread adoption of open table formats and open data models.
When data is stored in
open formats, organizations are no longer locked into a single engine. Data can
reside in low-cost object storage and be accessed by multiple compute services,
each optimized for different workloads; flexibility increases, but so does
complexity.
In an open
environment, the DBMS can no longer function as the central gatekeeper, so governance
must become independent of any single execution layer.
At the same time, AI
has raised the stakes. Enterprises are increasingly recognizing that
fragmented, poorly governed data leads directly to unreliable models, inflated
costs, and failed AI initiatives. Thus, governed AI requires consistent access
control, lineage, and policy enforcement across platforms and engines.
As a result, the governance catalog is becoming the natural control plane, one that operates across systems rather than within them.
Open Governance and the Decoupling of
Value
An important nuance in
this transition is that the point of control is shifting, but the point
of value is not necessarily following it.
Allow me to explain: as
governance capabilities mature, much of the core catalog functionality, such as technical
metadata, policy definition, and access mediation, is increasingly being standardized
and, in many cases, opened. This reduces differentiation at the governance layer
itself.
The implication is
clear: competitive advantage is moving higher in the stack. Governance then becomes
foundational infrastructure, essential but not where long-term value is
captured.
The value still resides in the toolchains, workflows, and application platforms that leverage governed data to deliver business outcomes, but governance is claiming a piece of the cake. At least until today.
From Data Platforms to Intelligent
Application Platforms
This architectural
shift sets the stage, in my view, for the next evolution of data platforms.
Rather than producing
isolated analytic artifacts like reports, dashboards, or tables, platforms are
increasingly expected to support systems of models. These systems combine
governed data, analytics, and AI into coordinated decision-making capabilities,
and in some cases, into multi-agent systems that act autonomously within
defined guardrails.
Of course, none of
this is feasible, or at least it is more difficult, without a strong governance
control strategy and structure. Intelligent applications depend on consistent
access rules, explainability, and trust across data sources and execution
environments.
It is in this sense that the migration of control to the governance layer is not an end in itself; it is a prerequisite for the next generation of enterprise applications dealing with more complex data and systems interactions.
So…
What we are witnessing
is a quiet but profound architectural realignment, which took a bit longer
than I expected.
The DBMS is no longer
the unquestioned center of control; this role is being assumed by the
governance layer, shaped by open data models and the operational demands of AI.
At the same time,
differentiation is moving upward, toward intelligent application platforms that
turn governed data into decisions and actions; decision intelligence,
for example, comes to mind.
The real competition,
then, is not over who owns the database or the catalog, but over who defines
the platform on which intelligent systems are built.
And that contest is only beginning.
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